(#74) Convert relative imports to absolute imports for improved modul…

…e compatibility.

README.md

@@ -59,12 +59,13 @@ from mpmath import power as pw
 from typing import List
 
 from pycellga.optimizer import cga
-from pycellga.recombination import ByteOnePointCrossover
-from pycellga.mutation import ByteMutationRandom
-from pycellga.selection import TournamentSelection
-from pycellga.problems import AbstractProblem
+from pycellga.recombination.byte_one_point_crossover import ByteOnePointCrossover
+from pycellga.mutation.byte_mutation_random import ByteMutationRandom
+from pycellga.selection.tournament_selection import TournamentSelection
+from pycellga.problems.abstract_problem import AbstractProblem
 from pycellga.common import GeneType
 
+
 class ExampleProblem(AbstractProblem):
 
     def __init__(self, n_var):

docs/pycellga.problems.rst

@@ -47,8 +47,9 @@ Below is an example of how to create a custom optimization problem by inheriting
 
 .. code-block:: python
 
-   from pycellga.problems import AbstractProblem
+   from pycellga.problems.abstract_problem import AbstractProblem
    from pycellga.common import GeneType
+
    from typing import List
    import numpy as np
 

docs/usage_examples.rst

@@ -20,10 +20,10 @@ Here’s how we can define this problem in Python using the `ExampleProblem` cla
     from typing import List
 
     from pycellga.optimizer import cga
-    from pycellga.recombination import ByteOnePointCrossover
-    from pycellga.mutation import ByteMutationRandom
-    from pycellga.selection import TournamentSelection
-    from pycellga.problems import AbstractProblem
+    from pycellga.recombination.byte_one_point_crossover import ByteOnePointCrossover
+    from pycellga.mutation.byte_mutation_random import ByteMutationRandom
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.problems.abstract_problem import AbstractProblem
     from pycellga.common import GeneType
 
     class ExampleProblem(AbstractProblem):
@@ -83,10 +83,10 @@ The Alpha Cellular Genetic Algorithm optimizes a real-valued problem using Blxal
     from typing import List
 
     from pycellga.optimizer import alpha_cga
-    from pycellga.recombination import BlxalphaCrossover
-    from pycellga.mutation import FloatUniformMutation
-    from pycellga.selection import TournamentSelection
-    from pycellga.problems import AbstractProblem
+    from pycellga.recombination.blxalpha_crossover import BlxalphaCrossover 
+    from pycellga.mutation.float_uniform_mutation import FloatUniformMutation 
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.problems.abstract_problem import AbstractProblem
     from pycellga.common import GeneType
 
 
@@ -130,11 +130,10 @@ The Alpha Cellular Genetic Algorithm optimizes a real-valued problem using Blxal
 The Compact Cellular Genetic Algorithm optimizes a binary problem where the goal is to maximize the number of `1`s.
 
 .. code-block:: python
-
     
     from pycellga.optimizer import ccga
-    from pycellga.selection import TournamentSelection
-    from pycellga.problems import AbstractProblem
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.problems.abstract_problem import AbstractProblem
     from pycellga.common import GeneType
 
     class ExampleProblem(AbstractProblem):
@@ -174,8 +173,8 @@ The Machine-Coded Compact Cellular Genetic Algorithm solves real-valued optimiza
     import numpy as np
 
     from pycellga.optimizer import mcccga
-    from pycellga.selection import TournamentSelection
-    from pycellga.problems import AbstractProblem
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.problems.abstract_problem import AbstractProblem
     from pycellga.common import GeneType
 
     class RealProblem(AbstractProblem):
@@ -216,10 +215,10 @@ The Synchronous Cellular Genetic Algorithm optimizes a real-valued problem in a
     from typing import List
 
     from pycellga.optimizer import sync_cga
-    from pycellga.recombination import BlxalphaCrossover
-    from pycellga.mutation import FloatUniformMutation
-    from pycellga.selection import TournamentSelection
-    from pycellga.problems import AbstractProblem
+    from pycellga.recombination.blxalpha_crossover import BlxalphaCrossover 
+    from pycellga.mutation.float_uniform_mutation import FloatUniformMutation 
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.problems.abstract_problem import AbstractProblem
     from pycellga.common import GeneType
 
 
@@ -335,7 +334,7 @@ For permutation-based optimization problems, the following option is available:
 
 .. code-block:: python 
 
-    from problems.single_objective.discrete.permutation.tsp import Tsp
+    from pycellga.problems.single_objective.discrete.permutation.tsp import Tsp
 
 
 These built-in problems provide a diverse set of test cases, allowing users to explore `pycellga`'s capabilities across a wide range of optimization challenges. Users can also define custom problems to suit their specific needs.
@@ -347,8 +346,8 @@ Choose from selection methods:
 
 .. code-block:: python 
 
-    from selection.tournament_selection import TournamentSelection
-    from selection.roulette_wheel_selection import RouletteWheelSelection
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.selection.roulette_wheel_selection import RouletteWheelSelection
 
 
 **Recombination Operators**
@@ -395,10 +394,11 @@ Optimize a binary problem using tournament selection, one-point crossover, and b
 .. code-block:: python
 
     from pycellga.optimizer import cga
-    from problems.single_objective.discrete.binary.one_max import OneMax 
-    from pycellga.selection import TournamentSelection
-    from pycellga.recombination import OnePointCrossover
-    from pycellga.mutation import BitFlipMutation
+    from pycellga.problems.single_objective.discrete.binary.one_max import OneMax 
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.recombination.one_point_crossover import OnePointCrossover
+    from pycellga.mutation.bit_flip_mutation import BitFlipMutation
+
 
     result = cga(
         n_cols=5,
@@ -424,10 +424,10 @@ Solve a real-valued optimization problem using Byte One Point Crossover and Byte
 .. code-block:: python
 
     from pycellga.optimizer import cga
-    from problems.single_objective.continuous.ackley import Ackley
-    from pycellga.selection import RouletteWheelSelection
-    from pycellga.recombination import ByteOnePointCrossover
-    from pycellga.mutation import ByteMutation
+    from pycellga.problems.single_objective.continuous.ackley import Ackley
+    from pycellga.recombination.byte_one_point_crossover import ByteOnePointCrossover
+    from pycellga.selection.roulette_wheel_selection import RouletteWheelSelection
+    from pycellga.mutation.byte_mutation import ByteMutation 
 
     result = cga(
         n_cols=5,
@@ -452,10 +452,10 @@ Optimize a TSP using permutation encoding, PMX crossover, and swap mutation.
 .. code-block:: python
 
     from pycellga.optimizer import cga
-    from problems.single_objective.discrete.permutation.tsp import Tsp
-    from pycellga.selection import TournamentSelection
-    from pycellga.recombination import PMXCrossover
-    from pycellga.mutation import SwapMutation
+    from pycellga.problems.single_objective.discrete.permutation.tsp import Tsp
+    from pycellga.selection.tournament_selection import TournamentSelection
+    from pycellga.recombination.pmx_crossover import PMXCrossover
+    from pycellga.mutation.swap_mutation import SwapMutation
 
     result = cga(
         n_cols=5,

paper/paper.md

@@ -103,10 +103,10 @@ from mpmath import power as pw
 from typing import List
 
 from pycellga.optimizer import cga
-from pycellga.recombination import ByteOnePointCrossover
-from pycellga.mutation import ByteMutationRandom
-from pycellga.selection import TournamentSelection
-from pycellga.problems import AbstractProblem
+from pycellga.recombination.byte_one_point_crossover import ByteOnePointCrossover
+from pycellga.mutation.byte_mutation_random import ByteMutationRandom
+from pycellga.selection.tournament_selection import TournamentSelection
+from pycellga.problems.abstract_problem import AbstractProblem
 from pycellga.common import GeneType
 
 class ExampleProblem(AbstractProblem):

pycellga/__init__.py

@@ -1,12 +0,0 @@
-from . import example
-from . import mutation
-from . import neighborhoods
-from . import problems
-from . import recombination
-from . import selection
-from . import optimizer
-from . import population
-from . import individual
-from . import grid
-from . import byte_operators
-

pycellga/example/__init__.py

@@ -1,10 +0,0 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import optimizer
-from individual import GeneType
-from . import example_alpha_cga
-from . import example_ccga
-from . import example_cga
-from . import example_mcccga
-from . import example_sync_cga

pycellga/example/example_alpha_cga.py

@@ -1,16 +1,12 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
 from numpy import power as pw
 from typing import List
 
-from optimizer import alpha_cga
-from recombination import BlxalphaCrossover
-from mutation import FloatUniformMutation
-from selection import TournamentSelection
-from problems import AbstractProblem
-from common import GeneType
+from pycellga.optimizer import alpha_cga
+from pycellga.recombination.blxalpha_crossover import BlxalphaCrossover
+from pycellga.mutation.float_uniform_mutation import FloatUniformMutation
+from pycellga.selection.tournament_selection import TournamentSelection
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.common import GeneType
 
 
 class ExampleProblem(AbstractProblem):

pycellga/example/example_ccga.py

@@ -1,11 +1,8 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-from optimizer import ccga
-from selection import TournamentSelection
-from problems import AbstractProblem
-from common import GeneType
+
+from pycellga.optimizer import ccga
+from pycellga.selection.tournament_selection import TournamentSelection
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.common import GeneType
 
 class ExampleProblem(AbstractProblem):
 

pycellga/example/example_cga.py

@@ -1,16 +1,13 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
 from mpmath import power as pw
 from typing import List
 
-from optimizer import cga
-from recombination import ByteOnePointCrossover
-from mutation import ByteMutationRandom
-from selection import TournamentSelection
-from problems import AbstractProblem
-from common import GeneType
+from pycellga.optimizer import cga
+from pycellga.recombination.byte_one_point_crossover import ByteOnePointCrossover
+from pycellga.mutation.byte_mutation_random import ByteMutationRandom
+from pycellga.selection.tournament_selection import TournamentSelection
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.common import GeneType
 
 class ExampleProblem(AbstractProblem):
 

pycellga/example/example_mcccga.py

@@ -1,13 +1,10 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
 import numpy as np
 
-from optimizer import mcccga
-from selection import TournamentSelection
-from problems import AbstractProblem
-from common import GeneType
+from pycellga.optimizer import mcccga
+from pycellga.selection.tournament_selection import TournamentSelection
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.common import GeneType
 
 class RealProblem(AbstractProblem):
 

pycellga/example/example_sync_cga.py

@@ -1,16 +1,13 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
 from numpy import power as pw
 from typing import List
 
-from optimizer import sync_cga
-from recombination import BlxalphaCrossover
-from mutation import FloatUniformMutation
-from selection import TournamentSelection
-from problems import AbstractProblem
-from common import GeneType
+from pycellga.optimizer import sync_cga
+from pycellga.recombination.blxalpha_crossover import BlxalphaCrossover
+from pycellga.mutation.float_uniform_mutation import FloatUniformMutation
+from pycellga.selection.tournament_selection import TournamentSelection
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.common import GeneType
 
 
 class ExampleProblem(AbstractProblem):

pycellga/individual.py

@@ -1,13 +1,10 @@
-import sys
-import os
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
 
 from numpy import random
 import numpy as np
 import random as rd
-from problems.abstract_problem import AbstractProblem
-from common import GeneType
+
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.common import GeneType
 
 
 class Individual:

pycellga/mutation/__init__.py

@@ -1,21 +0,0 @@
-from . import bit_flip_mutation
-from . import byte_mutation
-from . import byte_mutation_random
-from . import float_uniform_mutation
-from . import insertion_mutation
-from . import mutation_operator
-from . import shuffle_mutation
-from . import swap_mutation
-from . import two_opt_mutation
-
-# -------------------------------- mutation -------------------------------- #
-from mutation.bit_flip_mutation import BitFlipMutation
-from mutation.byte_mutation import ByteMutation
-from mutation.byte_mutation_random import ByteMutationRandom
-from mutation.insertion_mutation import InsertionMutation
-from mutation.shuffle_mutation import ShuffleMutation
-from mutation.swap_mutation import SwapMutation
-from mutation.two_opt_mutation import TwoOptMutation
-from mutation.float_uniform_mutation import FloatUniformMutation
-from mutation.mutation_operator import MutationOperator
-# -------------------------------------------------------------------------- #

pycellga/mutation/bit_flip_mutation.py

@@ -1,7 +1,7 @@
 import numpy as np
-from individual import *
-from problems.abstract_problem import AbstractProblem
-from mutation.mutation_operator import MutationOperator
+from pycellga.individual import *
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.mutation.mutation_operator import MutationOperator
 
 class BitFlipMutation(MutationOperator):
     """

pycellga/mutation/byte_mutation.py

@@ -1,8 +1,9 @@
-import numpy as np
-from individual import *
-from problems.abstract_problem import AbstractProblem
 import struct
-from mutation.mutation_operator import MutationOperator
+import numpy as np
+
+from pycellga.individual import *
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.mutation.mutation_operator import MutationOperator
 
 class ByteMutation(MutationOperator):
     """

pycellga/mutation/byte_mutation_random.py

@@ -1,8 +1,9 @@
-import numpy as np
-from individual import *
-from problems.abstract_problem import AbstractProblem
 import struct
-from mutation.mutation_operator import MutationOperator
+import numpy as np
+
+from pycellga.individual import *
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.mutation.mutation_operator import MutationOperator
 
 class ByteMutationRandom(MutationOperator):
     """

pycellga/mutation/float_uniform_mutation.py

@@ -1,7 +1,8 @@
 import random
-from individual import *
-from problems.abstract_problem import AbstractProblem
-from mutation.mutation_operator import MutationOperator
+
+from pycellga.individual import *
+from pycellga.problems.abstract_problem import AbstractProblem
+from pycellga.mutation.mutation_operator import MutationOperator
 
 class FloatUniformMutation(MutationOperator):
     """